Optimal Drug Regimens for TB: An Integrated Computational/Experimental Approach

应聘者描述(由申请人提供)：应聘者具有理论物理和计算机科学背景。这位候选人的长期职业目标是将生物科学领域的严格培训与他的量化和计算机技能相结合，以加快结核病(TB)治疗的后期药物开发进程。目前的目标是开发改进的计算方法和工具，以确定处于临床前开发阶段的结核病新的最佳组合疗法。这一职业发展奖是为了支持一项研究和培训计划，该计划包括密集的课程作业、指导阅读、参加研讨会、会议和研讨会，以及一项研究计划，该计划提供了对治疗结核病的宿主-药物-病原体相互作用的充分了解。 候选人将得到科罗拉多州立大学(CSU)微生物学、免疫学和病理学系的支持。这项提案的导师和合作者包括分枝杆菌研究实验室(MRL)、环境和放射健康科学系、化学和生物工程系以及兽医和生物医学学院临床科学系的教职员工。CSU是传染病研究的领先者，MRL拥有19名教职员工和100多名全职工作人员，是世界上最大的专注于分枝杆菌研究的组织，为将其引入结核病研究领域，并为该项目的成功提供了理想的环境。 为了给结核病新药方案的临床试验提供指导，在临床前开发阶段，显然需要一种有效的方法来确定最优的联合方案。虽然传统的药代动力学/药效学(PK/PD)方法已被证明有助于确定最佳的单一药物抗菌方案，但它们是数据密集型的，对于系统和彻底评估治疗结核病所需的3-和4种药物方案的效用有限。因此，这项研究计划的目的和目标是开发和演示一种计算框架的使用，该计算框架提供了治疗小鼠结核分枝杆菌感染的最佳组合药物剂量方案。提出的框架是基于生理的数学建模、贝叶斯推理、有针对性的实验研究和剂量优化的严格方法的新集成。这项建议的具体目的是：(1)在小鼠结核病感染模型中建立宿主-药物-病原体相互作用和药物剂量优化的计算框架，以及(2)展示在小鼠结核病感染模型中确定最佳多药方案的计算框架的使用。虽然我们试图利用目前的一线抗结核药物(异烟肼、利福平、吡津酰胺)来确定该提案的可行性，但该项目的最终目的是将该方法应用于较新的抗结核病药物，以便预测最佳方案，以便在临床试验中进行测试。

DESCRIPTION (provided by applicant): The candidate in this K25 application has a background in theoretical physics and computer science. The long term career goal of this candidate is to combine rigorous training in the field of bioscience with his quantitative and computer skills in order to accelerate the late-stage drug development process for the treatment of tuberculosis (TB). The immediate goal is to develop improved computational methods and tools to determine new optimal combination therapies for TB in the preclinical stage of development. This Career Development Award is requested to support a research and training program that includes intensive coursework, mentored readings, attendance at workshops, meetings and seminars, and a research plan that provides for a well-founded understanding of the host-drug-pathogen interactions for the treatment of TB. The candidate will be supported by the Department of Microbiology, Immunology and Pathology at Colorado State University (CSU). The mentors and collaborators for this proposal include faculty from the Mycobacteria Research Laboratories (MRL), the Department of Environmental & Radiological Health Sciences, Chemical & Biological Engineering, and the Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences. CSU is a leader in infectious disease research, and the MRL, with their 19 faculty and over 100 full-time staff, is the worlds largest group focused on mycobacterial research, providing an ideal environment to be introduced to the field of TB research, and for the success of this project. In order to provide guidance for clinical trial testing of new drug regimens for TB, there is a clear need for an efficient method to determine optimal combination regimens in the preclinical stage of development. While conventional pharmacokinetic/pharmacodynamic (PK/PD) methods have proven useful for the determination of optimal single drug antimicrobial regimens, they are data intensive and have limited utility for a systematic and thorough evaluation of the 3- and 4-drug regimens required to treat TB. The objective and goal of this research plan is therefore to develop and demonstrate the use of a computational framework which provides optimal combination drug dosage regimens for treatment of Mycobacterium tuberculosis infection in mice. The proposed framework is a novel integration of physiologically based mathematical modeling, Bayesian inference, targeted experimental studies, and a rigorous method for dose optimization. The specific aims of this proposal are to: (1) develop the computational framework for host-drug-pathogen interactions and drug dose optimization in mouse TB infection models, and (2) demonstrate the use of the computational framework for the determination of an optimal multidrug regimen in mouse TB infection models. While we seek to establish the feasibility of the proposal using current front-line anti-TB drugs (isoniazid, rifampin, pyrazinamide), the ultimate aim of this project is the application of the methodology to the newer anti-TB drugs in order to render predictions of optimal regimens for testing in clinical trials.